Designing interactive UI templates is a challenging task. Designers often struggle to determine the best design choices, and even experienced professionals spend significant time evaluating layouts. Moreover, assessing whether a design is good or bad for users remains difficult. This study aims to develop a framework for predicting and scoring the placement of interactive UI elements. By providing usability scores for element placement, our goal is to help trace the users in-teraction and make it more efficient and assist designers in optimizing their layouts. The You Only Look Once (YOLO) model is employed to detect interactive elements in UI screen-shots and evaluate their placement on a usability scale. The model predicts element positions and usability scores, enabling designers to refine their layouts based on data-driven insights. The model evaluates UI designs by identifying interactive elements and assigning usability scores. Designers can use these scores to assess the effectiveness of their layouts and make informed improvements without direct position suggestions. Our approach enhances UI usability, helping designers create more effective interfaces aligned with current design trends.

Abstract Service‐oriented architecture (SOA) has been widely used to design enterprise applications in the past two decades. The services in SOA are becoming complex with the increase in changing user requirements and SOA is still seen as monolithic from a deployment perspective. Monolithic services make the application complex, and it becomes difficult to maintain. With the evolution of microservices architecture, software architects started migrating legacy applications to microservices. However, existing migration approaches in the literature mostly focus on migrating monolithic applications to microservices. To the best of our knowledge, very few works have been done in migrating SOA applications to microservices. One of the major challenges in the migration process is the extraction of microservices from the existing legacy applications. To address this, we propose an approach to extract the candidate microservices using graph based algorithms. In particular, four algorithms are defined: (i) construction ofservice graph(SG), (ii) construction oftask graph(TG) for each service of the a SOA application, (iii) extraction of candidate microservices using the SG of SOA application, and (iv) construction of a SG for a microservices application to retain the dependencies between the generated microservices. We chose a SOA‐based web application to demonstrate the proposed microservices extraction approach and extracted the microservices. Additionally, we have evaluated the extracted microservices and compared them with SOA based services.

Service oriented architecture (SOA) has been widely used in the design of enterprise applications over the last two decades. Though SOA has become popular in the integration of multiple applications using the enterprise service bus, there are few challenges related to delivery, deployment, governance, and interoperability of services. To overcome the design and maintenance challenges in SOA, a new architecture of microservices has emerged with loose coupling, independent deployment, and scalability as its key features. With the advent of microservices, software architects have started to migrate legacy systems to microservice architecture. However, many challenges arise during the migration of SOA to microservices, including the decomposition of SOA to microservice, the testing of microservices designed using different programming languages, and the monitoring the microservices. In this paper, we aim to provide patterns for the most recurring problems highlighted in the literature i.e, the decomposition of SOA services, the size of each microservice, and the detection of anomalies in microservices. The suggested patterns are combined with our experience in the migration of SOA-based applications to the microservices architecture, and we have also used these patterns in the migration of other SOA applications. We evaluated these patterns with the help of a standard web-based application.